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©The Author(s) 2024.
World J Diabetes. Dec 15, 2024; 15(12): 2302-2310
Published online Dec 15, 2024. doi: 10.4239/wjd.v15.i12.2302
Published online Dec 15, 2024. doi: 10.4239/wjd.v15.i12.2302
Table 2 Diagnostic efficacy of artificial intelligence in screening diabetic retinopathy based on single direction fundus photography for each eye in natural population and diabetes population
| Different DR classifications | Natural population | People with diabetes | ||||
| AUC (95%CI) | Sensitivity (95%CI) | Specificity (95%CI) | AUC (95%CI) | Sensitivity (95%CI) | Specificity (95%CI) | |
| RDR | 0.936 (0.932-0.940) | 93.0% (85.4%-97.4%) | 94.2% (93.8%-94.6%) | 0.911 (0.900-0.922) | 94.0% (86.5%-98.0%) | 88.3% (86.9%-89.5%) |
| Different degrees of DR | 0.875 (0.870-0.880) | 79.3% (75.3%-82.9%) | 95.8% (95.4%-96.1%) | 0.891 (0.878-0.903) | 85.0% (79.9%-89.2%) | 93.2% (92.1%-94.2%) |
| Severe DR | 0.898 (0.893-0.902) | 85.7% (42.1%-99.6%) | 93.8% (93.4%-94.2%) | 0.929 (0.918-0.938) | 100.0% (47.8%-100.0%) | 85.8% (84.3%-87.1%) |
- Citation: Yao L, Cao CY, Yu GX, Shu XP, Fan XN, Zhang YF. Screening and evaluation of diabetic retinopathy via a deep learning network model: A prospective study. World J Diabetes 2024; 15(12): 2302-2310
- URL: https://www.wjgnet.com/1948-9358/full/v15/i12/2302.htm
- DOI: https://dx.doi.org/10.4239/wjd.v15.i12.2302